Detecting fraud by correlating user behavior biometrics with other data sources

ABSTRACT

One embodiment of the present invention sets forth a technique for predicting fraud by correlating user behavior biometric data with one or more other types of data. The technique includes receiving cursor movement data generated via a client device and analyzing the cursor movement data based on a model to generate a result. The model may be generated based on cursor movement data associated with a first group of one or more users. The technique further includes receiving log data generated via the client device and determining, based on the result and the log data, that a user of the client device is not a member of the first group.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the co-pending U.S. patentapplication titled, “DETECTING FRAUD BY CORRELATING USER BEHAVIORBIOMETRICS WITH OTHER DATA SOURCES,” filed on Apr. 17, 2017 and havingSer. No. 15/731,104. The subject matter of this related application ishereby incorporated herein by reference.

FIELD OF THE EMBODIMENTS

The various embodiments relate generally to computer security systemsand, more specifically, to fraud detection based on user behaviorbiometrics.

DESCRIPTION OF THE RELATED ART

Many web-based applications that provide services, such as financialservices and information technology (IT) management, implement one ormore types of security measures to prevent access by unauthorized users.For example, a web-based application may require a user to authenticatevia a username, password, digital certificate, token, etc. Althoughthese types of security measures generally deter unauthorized users fromaccessing sensitive information and resources, such security measurescannot, by themselves, prevent unauthorized access if the underlyingcredentials are compromised.

In order to detect whether a particular service is being accessed by anunauthorized user, such as when user credentials have been stolen,conventional techniques commonly compare characteristics of the user'scomputer to historical records to infer whether the user is anauthorized user. For example, many conventional techniques compare thecurrent Internet protocol (IP) address of the user's computer to the IPaddress(es) that have been used in the past to access the service.Additionally, some techniques may compare a device identifier (e.g., amedia access control (MAC) address) associated with the computer that isaccessing the service to a device identifier previously used by anauthorized user to access the service. Then, if one of these types ofvalues does not match a value associated with an authorized user, thenaccess to a service may be restricted.

One drawback to these approaches is that characteristics of a user'sdevice, such as an IP address and a device identifier, can be emulatedby an unauthorized user in order to evade conventional securitymeasures. Additionally, these types of approaches fail to prevent accessby unauthorized users that have obtained control of an authorized user'sphysical computer, either by physical or remote means. For example,conventional approaches cannot detect when an unauthorized user, such asa caretaker or cohabitant, accesses a service via an authorized user'scomputer system. Similarly, conventional approaches typically cannotdetect when a fraudster is remotely controlling an authorized user'scomputer to access a particular service.

As the foregoing illustrates, improved techniques for detectingfraudulent activity in computer systems would be useful.

SUMMARY

Embodiments of the present disclosure set forth a method for predictingfraud by correlating user behavior biometric data with one or more othertypes of data. The method includes receiving cursor movement datagenerated via a client device and analyzing the cursor movement databased on a model to generate a result. The model may be generated basedon cursor movement data associated with a first group of one or moreusers. The method further includes receiving log data generated via theclient device and determining, based on the result and the log data,that a user of the client device is not a member of the first group.

Further embodiments provide, among other things, a system and anon-transitory computer-readable storage medium configured to implementthe techniques set forth above.

At least one advantage of the techniques described herein is that userfraud can be detected based on criteria, such as user cursor movements,that cannot be readily emulated by a fraudster. Accordingly, fraud canbe more effectively detected, even when an attacker has taken physicalor remote control of an authorized user's computer. Further, bycorrelating the results of behavior biometric analysis with other datasources, such as log data received from a client device and/or one ormore server devices, the accuracy of fraud prediction can be improved.Finally, various techniques described herein are capable of effectivelygeneralizing patterns included in small datasets of behavior biometricdata, enabling more accurate comparisons to be made between the smalldatasets and behavior biometric data associated with an unknown user.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the recited features of the one or moreembodiments set forth above can be understood in detail, a moreparticular description of the one or more embodiments, brieflysummarized above, may be had by reference to certain specificembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments and are therefore not to be considered limiting ofits scope in any manner, for the scope of the various embodimentssubsumes other embodiments as well.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A and 7B are conceptual block diagrams of a biometrics analysissystem configured to interact with the data intake and query system ofFIG. 1 to implement one or more aspects of the disclosed embodiments;

FIG. 8 illustrates a flow diagram of method steps for predicting userfraud based on behavior biometric data in accordance with the disclosedembodiments;

FIG. 9A illustrates a technique for encoding cursor movement directionvia image parameters in accordance with the disclosed embodiments;

FIG. 9B illustrates a biometric image generated via the technique ofFIG. 9A in accordance with the disclosed embodiments;

FIG. 10 illustrates biometric images generated based on cursor datareceived from two different users in accordance with the disclosedembodiments;

FIG. 11 illustrates a flow diagram of method steps for training amachine learning model to predict whether behavior biometric databelongs to a user included in a group of one or more users in accordancewith the disclosed embodiments; and

FIG. 12 illustrates a flow diagram of method steps for training a neuralnetwork (NN) based on a small dataset of behavior biometric data inaccordance with the disclosed embodiments.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Security Features        -   2.11. Detecting Fraud Based On Behavior Biometric Data            -   2.11.1 Correlating Behavior Biometric Results with Log                Data            -   2.11.2 Generating and Training a Neural Network

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata.” In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5 ).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML, documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

2.10. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

2.11. Detecting Fraud Based on Behavior Biometric Data

As noted above, in order to detect whether a particular service is beingaccessed by an unauthorized user, such as when user credentials havebeen compromised, conventional techniques commonly comparecharacteristics of a computer being used to access the service to knownvalues associated with an authorized user. Based on thesecharacteristics of the computer that is accessing the service, aprediction is then made regarding whether the user is an authorized useror a fraudster.

However, these types of conventional techniques suffer from a number ofdrawbacks. In particular, fraudsters are becoming more and more capableof emulating various aspects of their interactions with web-basedservices and resources, such as by emulating an IP address and/or adevice identifier to match the values associated with an authorizeduser. Further, fraudsters may obtain control of an authorized user'sphysical computer, either via physical presence or remote means, suchthat the IP address and device identifier match the values expected by aparticular service. As a result, conventional techniques areinsufficient for detecting more advanced instances of user fraud.

Accordingly, various embodiments disclosed herein collect and analyzebehavior biometric data associated with an input device that is beingused to interact with a particular service or resource. In someembodiments, the behavior biometric data is then converted into one ormore images and compared to known images associated with a group of oneor more users, such as a group of one or more authorized users. Thesecomparisons may be made via one or more machine learning algorithms,such as by analyzing the images via a neural network (NN). A predictionis then made regarding whether the biometric data belongs to the groupof one or more users, for example, in order to determine whether a userassociated with the biometric data is likely to be a fraudster.

Further, in various embodiments, the biometric data may be correlatedwith one or more other types of data associated with a user and/or acomputing device that is accessing the service or resource. For example,a result of analyzing the biometric data (e.g., a score that indicates alikelihood of fraud) may be correlated with one or more other aspects ofthe user session, such as the screen resolution of the user's device, auser agent (e.g., a web browser) being implemented to access the serviceor resource, and/or the specific types of activities being performedduring the user session. A risk score may then be generated based on theanalysis of the biometric data and the one or more aspects of the usersession.

Additionally, in some embodiments, a small dataset of biometric data(e.g., less than 1,000 images of cursor movement data) associated with aknown group of one or more users may be implemented to predict fraud.For example, in various embodiments, the biometric techniques disclosedherein may be implemented to predict fraud in applications where anauthorized user or group of users infrequently accesses a particularservice or resource and, thus, only a small dataset of biometric data isavailable for the user or group of users. In such embodiments, variousaspects of a NN may be modified in order to improve the detectionaccuracy. For example, by implementing a dropout rate that is greaterthan 50% (e.g., a dropout rate greater than 75%), a NN may be forced togeneralize patterns included in a small dataset. Accordingly, by moreeffectively generalizing patterns included in images of biometric data,such as images of cursor movement, the NN techniques described hereinare able to more accurately predict whether a user is a member of aparticular group. These and other approaches are described below infurther detail in conjunction with FIGS. 7A, 7B, 8, 9A, 9B, and 10-12 .

FIGS. 7A and 7B are conceptual block diagrams of a biometrics analysissystem 700 configured to interact with the data intake and query system108 of FIG. 1 to implement one or more aspects of the disclosedembodiments. As shown in FIG. 7B, the biometrics analysis system 700includes a processor 720, storage 722, an input/output (I/O) devicesinterface 724, a network interface 726, an interconnect 730, and asystem memory 740. The memory 110 includes a fraud predictionapplication 741 and biometric images 746. The fraud predictionapplication 741 includes machine learning logic 742 and an imagegenerator 744.

The processor 720 may be any technically feasible form of processingdevice configured to process data and execute program code. Theprocessor 720 could include, for example, and without limitation, asystem-on-chip (SoC), a central processing unit (CPU), a graphicsprocessing unit (GPU), an application-specific integrated circuit(ASIC), a digital signal processor (DSP), a field-programmable gatearray (FPGA), and so forth. Processor 720 includes one or moreprocessing cores. In operation, processor 720 is the master processor ofbiometrics analysis system 700, controlling and coordinating operationsof other system components.

System memory 740 may include a memory module or a collection of memorymodules. The fraud prediction application 741 is executed by theprocessor 720 to implement the overall functionality of the biometricsanalysis system 700. For example, and without limitation, behaviorbiometric data (e.g., cursor movements and click events) acquired fromone or more client devices 102 via the data intake and query processingsystem 108 may be processed by the image generator 744 to generatebiometric images 746. The biometric images 746 may then be transmittedto the machine learning logic 742 to train a model (e.g., a neuralnetwork (NN)), to generate weights for a model, and/or to generatepredictions based on a model.

I/O devices interface 724 may receive and/or transmit data to/from inputdevices, output devices, and devices capable of both receiving input andproviding output. For example, and without limitation, I/O devicesinterface 724 could interface with wired and/or wireless communicationdevices that send data to and/or receive data.

In some embodiments, the biometrics analysis system 700 may beimplemented via one or more computing systems that are separate from,but in communication with, the data intake and query processing system108. In other embodiments, the biometrics analysis system 700 may beincluded in and/or implemented by the data intake and query processingsystem 108. In such embodiments, both the data intake and queryprocessing system 108 may be implemented via some or all of the samecomponents (e.g., the same processor(s) 720, storage 722, memory 740,etc.) in order to receive data from one or more client devices 102and/or host devices 106 and analyze the data to predict fraudulentbehavior. However, the embodiments disclosed herein contemplate anytechnically feasible system configured to implement the functionality ofthe biometrics analysis system 700.

FIG. 8 illustrates a flow diagram of method steps for predicting userfraud based on behavior biometric data in accordance with the disclosedembodiments. Although the method steps are described in conjunction withthe systems of FIGS. 1, 2, 7A, and 7B, persons skilled in the art willunderstand that any system configured to perform the method steps, inany order, falls within the scope of the present invention.

As shown in FIG. 8 , a method 800 begins at step 802, where the fraudprediction application 741 receives cursor data 714 from data intake andquery system 108. In various embodiments, cursor data 714 may becontained in one or more events, where each event may includetimestamped cursor data associated with a particular period of time. Forexample, as described herein, cursor data 714 and/or other types of data(e.g., client data 710) could be stored as one or more events thatcomprise machine-generated data and are associated with one or morespecific points in time. For example, events may be derived from “timeseries data” that corresponds to cursor locations, cursor events, etc.that are associated with successive points in time.

In general, the cursor data 714 may include any type of data indicativeof user interactions that are received via an input device (e.g., amouse, a touchpad, a touchscreen, etc.), including cursor movement data(e.g., timestamped (x,y) coordinates) and click events (e.g., leftclick, right click, click-and-drag, etc.). For example, each unit ofcursor data could include an x-coordinate, a y-coordinate, a timestamp,a userID, and/or an eventID (e.g., a type of click event). In someembodiments, cursor data is collected at millisecond intervals, such asevery 10 milliseconds.

In various embodiments, data intake and query system 108 may generatecursor data 714 based on client data 710 that is received from one ormore client devices 102. For example, monitoring component 112 couldinclude a forwarding script that receives cursor data and/or other typesof user session data that reflects user activity within a clientapplication 110. The forwarding script could then forward the cursordata and/or other user session data to the data intake and query system108 in the form of client data 710. In general, the forwarding scriptcould written in any programming language, including JavaScript, C++,etc. For example, the forwarding script could be a JavaScript snippetincluded in a web application, such as a financial services web portal,and the client data 710 could be transmitted in a JavaScript ObjectNotation (BON) format from the web application to the data intake andquery system 108. As noted above, the data intake and query system 108could then convert the client data 710 into one or more events and storethe events for later searching and retrieval.

Next, at step 804, fraud prediction application 741 generates one ormore biometric images 746 via image generator 744 based on the cursordata 714. The biometric image(s) 746 are then stored in system memory740 and/or storage 722 in any feasible format (e.g., Joint PhotographicExperts Group (JPEG) or Portable Network Graphics (PNG)). In variousembodiments, image generator 744 encodes the speed and/or direction ofcursor movements and/or click events included in the cursor data 714 inone or more image parameters. In some embodiments, as shown in FIG. 9A,cursor movement direction may be encoded via a color value, and cursormovement speed may be encoded via a transparency value.

For example, when cursor movement data indicates that a cursor was movedto the right at a low rate of speed (e.g., point 902 in FIG. 9A), thenthe resulting line of the cursor movement included in the biometricimage 746 generated by image generator 744 may be a red color having ahigh transparency value. In another example, when cursor movement dataindicates that a cursor was moved towards the bottom of a screen at alow rate of speed (e.g., point 904 in FIG. 9A), then the resulting lineof the cursor movement included in the biometric image 746 may be a bluecolor having a high transparency value. Alternatively, if, in either ofthe above examples, the cursor was moved to the right or towards thebottom of the screen at a higher rate of speed, then the resulting lineof the cursor movement included in the biometric image 746 would have alower transparency value, as shown at points 906 and 908, respectively.

In some embodiments, the image generator 744 encodes the speed of cursormovement on a transparency layer of an image. The image generator 744then flattens the transparency layer with one or more other layers, suchas a white background layer, so that fast cursor movements will bedefined by brighter, more desaturated colors, while slow cursormovements will be defined by darker, more saturated colors. The imagegenerator 744 may further flatten the resulting cursor movement linesonto a black background to generate a high-contrast image that can bemore easily analyzed by the machine learning logic 742. For example, asshown in FIG. 10 , which illustrates biometric images 746 generatedbased on cursor data received from two different users, differences inuser behavior are readily discernible when cursor movements and clickevents are displayed in a high-contrast image.

Further, in some embodiments, click events may be encoded via one ormore image parameters. For example, as shown in FIG. 9B, left clickevents and right click events could be encoded as geometric objects(e.g., circles, rectangles, polygons, freeform objects, etc.) havingdifferent colors, such as red and green. In addition, click-and-dragevents could be encoded via line thickness. For example, as shown inFIG. 9B, image generator 744 could generate a thicker line when cursordata 714 indicates that cursor movement occurred during a click-and-dragevent and a thinner line when cursor movement did not occur during aclick-and-drag event.

At step 806, the fraud prediction application 741 analyzes the biometricimage(s) 746 via machine learning logic 742 to generate a result. Invarious embodiments, at step 806, the machine learning logic 742includes a neural network (NN) that has been trained, based a dataset ofimages associated with a group of one or more users, to predict whethera particular biometric image 746 of cursor data 714 belongs to thatparticular group of one or more users.

In such embodiments, the biometric image(s) 746 generated at step 804are analyzed by the NN to generate a result, such as a probability thatthe cursor data 714 was generated by a fraudster. In some embodiments,the method 800 may then proceed to step 810, where the fraud predictionapplication 741 predicts, based on the result, whether the userassociated with the biometric image(s) 746 is a member of the group.Alternatively or additionally, the fraud prediction application 741could transmit the result to the data intake and query system 108, whichcould then predict whether the user associated with the biometricimage(s) 746 is a member of the group. One or more actions could then betaken if the fraud prediction application 741 and/or data intake andquery system 108 predicts that the user associated with the biometricimage(s) 746 is not a member of the group, including restricting theuser's access to a service, transmitting an alert to an authorized useror administrator, and/or transmitting a challenge-response prompt to theclient device being operated by the user.

In some embodiments, a probability (e.g., of fraud) of 70% or greatercould indicate that the biometric image 746 is associated with a userthat is not a member of the group of one or more users. On the otherhand, a probability of less than 50% could indicate that the biometricimage 746 is associated with a user that is a member of the group of oneor more users. Additionally, a probability of ˜50% to ˜70% couldcorrespond to an indeterminate result.

In some embodiments, at step 806, the result could indicate that thecursor data was likely generated by a script attack, such as when afraudster programs a script to interact with a service or resource in aspecific manner (e.g., to perform a financial transaction quickly). Forexample, at step 806, the fraud prediction application 741 could analyzethe biometric image(s) 746 and/or the cursor data 714 and determine thatsubstantially no cursor movements occurred, indicating that a scriptattack was likely implemented. The fraud prediction application 741could then generate a result indicating that the user is likely anunauthorized user and/or not a member of the group and output the resultto the data intake and query system 108.

In various embodiments, the group of one or more users may include asingle user (e.g., an authorized user of a particular account), or thegroup may include users having similar behavioral characteristics. Forexample, the group may include users that have similar age ranges, usersof the same gender, users having similar employment and/or skill sets,and/or users that have some other relationship that is indicative ofuser behavior biometrics. In a specific example, a group of one or moreusers may include users that are known to be authorized users of aparticular financial service or IT management portal. Because each userin the group is authorized to use the portal, each user is more likelyto interact with the portal (e.g., via an input device) in a similarmanner. For example, authorized users of a banking service portaltypically perform similar types of actions, such as viewing an accountbalance and paying bills. Further, authorized users are more likely tobe familiar with the interface associated with a portal and, thus, arelikely to interact with the interface in a more controlled manner.

By contrast, unauthorized users, such as fraudsters, may performdifferent types of activities, such as changing a correspondenceaddress, changing a password, changing notifications preferences, and/orperforming an unusual financial transaction. Additionally, unauthorizedusers are less likely to be familiar with the interface associated witha portal and, thus, are likely to interact with the interface in a moreerratic manner. Consequently, in many cases, cursor data generated byauthorized users is likely to differ from cursor data generated byunauthorized users.

In some embodiments, machine learning logic 742 could be generated basedon a convolutional NN model (e.g., a Visual Geometry Group (VGG) model)that is capable of performing image recognition. Different approachesfor generating and training NN models, such as NN models that are basedon VGG-16 and VGG-19 models, based on biometric images 746 associatedwith one or more groups of users are described below in further detailin conjunction with FIGS. 11 and 12 .

2.11.1 Correlating Behavior Biometric Results with Log Data

Returning to step 806, in some embodiments, after the biometric image(s)746 are analyzed by the machine learning logic 742, the method 800 couldproceed to optional step 808, where the fraud prediction application 741receives log data 716 associated with a client device 102 being operatedby the user. In general, log data may include any type of dataassociated with a user session (e.g., a financial services session, anIT management session, etc.) and/or any type of data associated with aclient device 102 that is being implemented to interact with aparticular service or resource. In some embodiments, the log data isdata 712 is transmitted from one or more host devices 106 to the dataintake and query system 108 and optionally stored as one or more events.

Some of specific examples of log data 716 that could be received byfraud prediction application 741 at step 808 include a screen resolutionof the client device 102 that is accessing a service or resource, an IPaddress of the client device 102, an identifier (e.g., MAC address) ofthe client device 102, a type of web browser implemented by the clientdevice 102, and one or more types of activities being performed via theclient device 102. For example, log data could indicate that aparticular type of financial transaction has been performed via theclient device 102 and/or that a change to a user address, a password,and/or a notification preference has been made via the client device102.

Next, at step 810, when implementing optional step 808, the fraudprediction application 741 would predict, based on both the result andthe log data, whether the user is a member of the group of one or moreusers. In some embodiments, the fraud prediction application 741 couldpredict whether the user is a member of group based on whether theresult is above a threshold level (e.g., 70%) and based on whether oneor more aspects of the log data match one or more known aspects of anaccount associated with an authorized user. For example, if the fraudprediction application 741 determines that the result is above thethreshold level, then the fraud prediction application 741 coulddetermine whether two or more of a screen resolution, a web browser, andan IP address associated with the client device 102 do not match a knownscreen resolution, a known web browser, and/or a known IP addressassociated with a user account. Then, if two or more aspects of the logdata do not match known aspects of the authorized user's account, thefraud prediction application 741 could determine that the user is likelyto be a fraudster.

Although steps 808 and 810 are described above as being performed by thefraud prediction application 741, in some embodiments, one or both ofthese steps may be performed by the data intake and query system 108.For example, the data intake and query system 108 could receive a resultfrom the fraud prediction application 741 and then correlate the resultwith log data received from one or more clients devices 102 and/or hostdevices 106 in order to predict, based on any of the techniquesdescribed herein, whether a user is a member of particular group. Thedata intake and query system 108 could then perform one or more actionsbased on the prediction (e.g., restricting access to a service, issuinga challenge-response prompt, and/or transmitting an alert).

2.11.2 Generating and Training a Neural Network

FIG. 11 illustrates a flow diagram of method steps for training amachine learning model to predict whether behavior biometric databelongs to a user included in a group of one or more users in accordancewith the disclosed embodiments. Although the method steps are describedin conjunction with the systems of FIGS. 1, 2, 7A, and 7B, personsskilled in the art will understand that any system configured to performthe method steps, in any order, falls within the scope of the presentinvention.

As shown in FIG. 11 , a method 1100 begins at step 1102, where the fraudprediction application 741 receives cursor data 714 from members and/ornon-members of the group of one or more users. In various embodiments,cursor data 714 may be received by the fraud prediction application 741in a manner that is similar to the manner in which cursor data 714 isreceived at step 802, as described above in conjunction with FIG. 8 .For example, cursor data 714 could be received via one or more eventsthat include timestamped cursor movement data and/or click events.

Then, at step 1104, image generator 744 generates biometric images 746based on the cursor data 714, for example, in a manner that is similarto the manner in which biometric images 746 are generated at step 804,as described above in conjunction with FIG. 8 . In some embodiments,biometric images 746 that are generated based on cursor data 714associated with members of the group are stored in a directory that isseparate from a directory in which biometric images 746 generated basedon cursor data 714 associated with non-members of the group are stored.

Next, at step 1106, the fraud prediction application 741 trains themachine learning model 742 based on biometric images 746 associated withmembers of the group and/or non-members of the group. For example, themachine learning model 742 could analyze biometric images 746 associatedwith members of the group (e.g., stored in a first directory) to detectone or more patterns associated with that group, and then analyzebiometric images 746 associated with non-members of the group (e.g.,stored in a second directory) to detect one or more different patternsthat are associated with non-members of the group. In variousembodiments, training of the machine learning model 742 results in a setof model weights, which can then be implemented to validate the machinelearning model 742 and generate predictions via the machine learningmodel 742.

As noted above, the machine learning model 742 may include a NN model,such a convolutional NN model. As further noted above, in variousembodiments, a limited number of biometric images 746 may be availablefor a particular group of one or more users. For example, in someimplementations, fewer than 1,000 biometric images 746 may be availablefor a particular group, while, in other implementations fewer than 300biometric images 746 may be available for a particular group.Consequently, in order to effectively train the NN model with thislimited number of biometric images 746, certain NN parameters may beimplemented by the fraud prediction application 741. An example of suchNN parameters is described below in further detail in conjunction withFIG. 12 .

At step 1108, the fraud prediction application 741 validates the machinelearning model 742 based on the model weights generated at step 1106.For example, the machine learning model 742 may be validated byanalyzing a set of one or more biometric images that are known to belongto member(s) or non-member(s) of the group. Accordingly, the accuracy ofthe machine learning model 742 and model weights can be determined.After validation is complete, the method 1100 proceeds to step 1110,where the fraud prediction application 741 stores the model weights and,optionally, the validation results associated with the model weights tosystem memory 740 or storage 722. The model weights may then beretrieved by the fraud prediction application 741 and implemented by themachine learning model 742 to analyze biometric images 746 in order togenerate results, as described above in conjunction with step 806 ofFIG. 8 .

2.11.3 Neural Network Parameters for Small Datasets

FIG. 12 illustrates a flow diagram of method steps for training a neuralnetwork (NN) based on a small dataset of behavior biometric data inaccordance with the disclosed embodiments. Although the method steps aredescribed in conjunction with the systems of FIGS. 1, 2, 7A, and 7B,persons skilled in the art will understand that any system configured toperform the method steps, in any order, falls within the scope of thepresent invention. As noted above, the techniques described herein couldbe implemented with a convolutional NN model, such as a VGG-16 modeland/or a VGG-19 model. However, in various embodiments, any type of NNmodel may be implemented.

As shown in FIG. 12 , a method 1200 begins at step 1202, where the fraudprediction application 741 applies a two-dimensional (2D) convolutionlayer to a representation of the biometric image 746 to generate atensor of outputs. At step 1204, the fraud prediction application 741applies a 2D pooling layer to the representation of the biometric image746, for example, in order to reduce the spatial size and/or number ofparameters of the representation of the biometric image 746. At step1206, the fraud prediction application 741 may determine that steps 1202and 1204 should be repeated a given number of times, such as threetimes. Accordingly, the method 1200 returns to steps 1202 and 1204,where a 2D convolution layer and a 2D pooling layer are again applied tothe representation of the biometric image 746

Next, at step 1208, the fraud prediction application 741 generates andtrains a dense NN having a particular number of neurons, such asapproximately 32 neurons to 4096 neurons, or, in some embodiments,approximately 64 neurons to 512 neurons. Then, at step 1210, the fraudprediction application 741 discards a specified percentage of the units(e.g., neurons and/or activations).

In some embodiments, in order to more effectively recognize patterns insmall datasets, a discard rate (also referred to as a dropout rate) ofgreater than 50% may be implemented in the machine learning model 742.For example, in some embodiments, a discard rate of 75% may beimplemented in order to force the NN to retrain itself and generalizepatterns included in a biometric image 746. Accordingly, higherprediction probabilities and greater prediction accuracy is achievedwhen comparing the small dataset to incoming biometric images 746. It isfurther noted that implementing a discard rate of greater than 50%(e.g., greater than 75%) stands in stark contrast to the discard ratesthat are typically implemented in conventional techniques, whichgenerally discard units at a rate of less than 50%.

At step 1212, the fraud prediction application 741 may determine thatsteps 1208 and 1210 should be repeated a given number of times, such asthree times. For example, in some embodiments, in a first iteration ofsteps 1208 and 1210, 64 neurons could be generated at step 1208, and adiscard rate of 75% or greater could be implemented at step 1210. Then,in a second iteration of steps 1208 and 1210, 128 neurons could begenerated at step 1208, and a discard rate of 75% or greater could beimplemented at step 1210. Finally, in a third iteration of steps 1208and 1210, 512 neurons could be generated at step 1208, and a discardrate of 75% or greater could be implemented at step 1210. Theseparticular NN parameters have been demonstrated as being resilient tooverfitting and providing high prediction accuracy when implemented inconjunction with small datasets of biometric images 746, such asdatasets that include approximately 180 to 1,000 biometric images 746.

In sum, a fraud prediction application generates one or more imagesbased on behavior biometric data and compares the images, via a machinelearning model, to known images associated with a group of one or moreauthorized users. A prediction is then made regarding whether thebehavior biometric data belongs to the group of one or more users.

Further, in various embodiments, the fraud prediction application maycorrelate a result of analyzing the behavior biometric data with one ormore other types of data associated with a user and/or a computingdevice that is accessing a service or resource. A risk score may then begenerated based on the result and the one or more other types of data.

Additionally, in some embodiments, a small dataset of biometric dataassociated with a known group of one or more users may be implemented topredict fraud. In such embodiments, various aspects of a NN may bemodified in order to improve the detection accuracy, such as byimplementing a dropout rate that is greater than 50% (e.g., a dropoutrate greater than 75%) in order to force the NN to generalize patternsincluded in a small dataset.

At least one advantage of the disclosed techniques is that user fraudcan be detected based on criteria, such as user cursor movements, thatcannot be readily emulated by a fraudster. Accordingly, fraud can bemore effectively detected, even when an attacker has taken physical orremote control of an authorized user's computer. Further, by correlatingthe results of behavior biometric analysis with other data sources, suchas log data received from a client device and/or one or more serverdevices, the accuracy of fraud prediction can be improved. Finally,various techniques described herein are capable of effectivelygeneralizing patterns included in small datasets of behavior biometricdata, enabling more accurate comparisons to be made between the smalldatasets and behavior biometric data associated with an unknown user.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, enable the implementation of the functions/acts specified inthe flowchart and/or block diagram block or blocks. Such processors maybe, without limitation, general purpose processors, special-purposeprocessors, application-specific processors, or field-programmable

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

The invention claimed is:
 1. A computer-implemented method, comprising:receiving a first set of cursor movement data associated with one ormore cursor movements of a cursor that are acquired by a client deviceduring a first period; generating, based on the first set of cursormovement data, a first two-dimensional image that includes a set ofencoded lines corresponding to distinct portions of the first set ofcursor movement data; and training, based on the first two-dimensionalimage, a first neural network to generate a trained first neural networkthat classifies two-dimensional image inputs, wherein: the trained firstneural network processes, via one or more model weights, a first inputtwo-dimensional image to generate a first score that, in combinationwith data associated with a client device, indicates a first likelihoodof fraudulent activity, wherein the data associated with the clientdevice includes at least one of a screen resolution of the clientdevice, an internet protocol (IP) address of the client device, a mediaaccess control (MAC) address of the client device, or a type of webbrowser implemented by the client device, processing the firsttwo-dimensional image causes the first neural network to modify the oneor more model weights to generate one or more modified model weights,wherein the one or more modified model weights improve detectionaccuracy of the first neural network relative to the one or more modelweights, and the trained first neural network processes, via one or moremodel weights, a second input two-dimensional image to generate a secondscore that indicates a second likelihood of fraudulent activity and isdifferent than the first score.
 2. The computer-implemented method ofclaim 1, wherein the first set of cursor movement data includes at leastone of: coordinate data associated with the one or more cursor movementsduring the first period; speeds of the cursor for a set of coordinates;or directions of the one or more cursor movements for the set ofcoordinates.
 3. The computer-implemented method of claim 2, whereingenerating the first two-dimensional image comprises generating anencoding for the first set of cursor movement data to produce the set ofencoded lines, the encoding including one or more image parametersrepresenting the speeds of the cursor or the directions of the one ormore cursor movements.
 4. The computer-implemented method of claim 3,wherein the one or more image parameters representing the speeds of thecursor or the directions of the one or more cursor movements compriseone of a color value, a transparency value, a saturation value, or aluminance value.
 5. The computer-implemented method of claim 1, whereinprocessing a plurality of images causes the first neural network tofurther modify the one or more model weights; a first subset of theplurality of images are associated with a first group of users; a secondsubset of the plurality of images is not associated with the first groupof users; and the first score indicates a likelihood that a user is amember of the first group of users.
 6. The computer-implemented methodof claim 1, wherein training the first neural network comprises using adropout rate that is greater than 50%.
 7. The computer-implementedmethod of claim 1, further comprising: receiving a second set of cursormovement data generated during a second period; generating, based on thesecond set of cursor movement data, a second two-dimensional image;inputting the second two-dimensional image into the trained first neuralnetwork to generate the second score; receiving log data associated witha device that generated the second set of cursor movement data duringthe second period; generating, based on the log data including a valuefor a field that does not correspond to a parameter for the field, aresult value, wherein the parameter corresponds to an account associatedwith a member of a group of users; determining, based on the resultvalue and the second score, that a first user associated with the logdata and the second set of cursor movement data, is not included in thegroup of users; and performing, based on determining that the first useris not included in the group of users, a preventative action.
 8. Thecomputer-implemented method of claim 7, wherein the log data comprisesone of a characteristic of the device, or an activity performed via thedevice; and the log data includes at least one value for the field froma set of: (i) a screen resolution, (ii) a web browser implemented by thedevice, or (iii) an Internet protocol (IP) address of the device.
 9. Thecomputer-implemented method of claim 1, wherein the trained first neuralnetwork comprises a convolutional neural network, or is based on aVisual Geometry Group (VGG) neural network.
 10. The computer-implementedmethod of claim 1, wherein: the first set of cursor movement datacomprises a plurality of events; each event included in the plurality ofevents comprises a sequence of timestamped cursor coordinates; theplurality of events is stored in one or more field-searchable datastores; and receiving the first set of cursor movement data comprisesextracting, at search time, the first set of cursor movement data fromone or more fields included in the events.
 11. The computer-implementedmethod of claim 1, further comprising storing at least one of themodified one or more model weights or the second score in a memory, suchthat the at least one of the modified one or more model weights or thesecond score are accessible by the trained first neural network toprocess a third input two-dimensional image to generate a third scorethat indicates a third likelihood of fraudulent activity.
 12. One ormore non-transitory computer-readable media storing instructions that,when executed by one or more processors, cause the one or moreprocessors to perform steps of: receiving a first set of cursor movementdata associated with one or more cursor movements of a cursor that areacquired by a first client device during a first period; generating,based on the first set of cursor movement data, a first two-dimensionalimage that includes a set of encoded lines corresponding to distinctportions of the first set of cursor movement data; and training, basedon the first two-dimensional image, a first neural network to generate atrained first neural network that classifies two-dimensional imageinputs, wherein: the trained first neural network processes, via one ormore model weights, a first input two-dimensional image to generate afirst score that, in combination with data associated with a clientdevice, indicates a first likelihood of fraudulent activity, wherein thedata associated with the client device includes at least one of a screenresolution of the client device, an internet protocol (IP) address ofthe client device, a media access control (MAC) address of the clientdevice, or a type of web browser implemented by the client device,processing the first two-dimensional image causes the first neuralnetwork to modify the one or more model weights to generate one or moremodified model weights, wherein the one or more modified model weightsimprove detection accuracy of the first neural network relative to theone or more model weights, and the trained first neural networkprocesses, via one or more model weights, a second input two-dimensionalimage to generate a second score that indicates a second likelihood offraudulent activity and is different than the first score.
 13. The oneor more non-transitory computer-readable media of claim 12, wherein: thefirst set of cursor movement data includes at least one of: coordinatedata associated with the one or more cursor movements during the firstperiod; speeds of the cursor fora set of coordinates; or directions ofthe one or more cursor movements for the set of coordinates; generatingthe first two-dimensional image comprises generating an encoding for thefirst set of cursor movement data to produce the set of encoded lines;the encoding includes one or more image parameters representing thespeeds of the cursor or the directions of the one or more cursormovements; and the one or more image parameters representing the speedsof the cursor or the directions of the one or more cursor movementscomprise one of a color value, a transparency value, a saturation value,or a luminance value.
 14. The one or more non-transitorycomputer-readable media of claim 12, wherein the trained first neuralnetwork includes at least one two-dimensional layer for processing thefirst two-dimensional image.
 15. The one or more non-transitorycomputer-readable media of claim 12, wherein processing the firsttwo-dimensional image to modify the one or more model weights comprisesprocessing the first two-dimensional image through at least a firstconvolutional layer.
 16. The one or more non-transitorycomputer-readable media of claim 12, wherein processing the firsttwo-dimensional image to modify the one or more model weights comprises:processing the first two-dimensional image via a first convolutionallayer to generate a first set of weights; processing the first set ofweights via a pooling layer to generate a second set of weights;processing the second set of weights via a flattening layer to generatea third set of weights; processing the third set of weights via a denselayer to generate a fourth set of weights; and processing the fourth setof weights via a dropout layer to generate a fifth set of weights,wherein the first set of weights, the second set of weights, the thirdset of weights, the fourth set of weights, and the fifth set of weightsare included in the one or more model weights.
 17. The one or morenon-transitory computer-readable media of claim 12, wherein: processinga plurality of images causes the first neural network to further modifythe one or more model weights, and the plurality of images comprisesless than 1000 images and includes the first two-dimensional image. 18.The one or more non-transitory computer-readable media of claim 12,wherein: processing a plurality of images causes the first neuralnetwork to further modify the one or more model weights; a first subsetof the plurality of images are associated with a first group of users; asecond subset of the plurality of images is not associated with thefirst group of users; and the first score indicates a likelihood that auser is a member of the first group of users.
 19. A fraud predictionsystem, comprising: a memory storing a fraud prediction application; anda processor coupled to the memory that executes the fraud predictionapplication by: receiving a first set of cursor movement data associatedwith one or more cursor movements of a cursor that are acquired by afirst client device during a first period; generating, based on thefirst set of cursor movement data, a first two-dimensional image thatincludes a set of encoded lines corresponding to distinct portions ofthe first set of cursor movement data; and training, based on the firsttwo-dimensional image, a first neural network to generate a trainedfirst neural network that classifies two-dimensional image inputs,wherein: the trained first neural network processes, via one or moremodel weights, a first input two-dimensional image to generate a firstscore that, in combination with data associated with a client device,indicates a first likelihood of fraudulent activity, wherein the dataassociated with the client device includes at least one of a screenresolution of the client device, an internet protocol (IP) address ofthe client device, a media access control (MAC) address of the clientdevice, or a type of web browser implemented by the client device,processing the first two-dimensional image causes the first neuralnetwork to modify the one or more model weights to generate one or moremodified model weights, wherein the one or more modified model weightsimprove detection accuracy of the first neural network relative to theone or more model weights, and the trained first neural networkprocesses, via one or more model weights, a second input two-dimensionalimage to generate a second score that indicates a second likelihood offraudulent activity and is different than the first score.
 20. The fraudprediction system of claim 19, wherein: the first set of cursor movementdata includes at least one of: coordinate data associated with the oneor more cursor movements during the first period; speeds of the cursorfora set of coordinates; or directions of the one or more cursormovements for the set of coordinates; generating the firsttwo-dimensional image comprises generating an encoding for the first setof cursor movement data to produce the set of encoded lines; theencoding includes one or more image parameters representing the speedsof the cursor or the directions of the one or more cursor movements; andthe one or more image parameters representing the speeds of the cursoror the directions of the one or more cursor movements comprise one of acolor value, a transparency value, a saturation value, or a luminancevalue.
 21. The fraud prediction system of claim 19, wherein: processinga plurality of images causes the first neural network to further modifythe one or more model weights; a first subset of the plurality of imagesare associated with a first group of users; a second subset of theplurality of images is not associated with the first group of users; andthe first score indicates a likelihood that a user is a member of thefirst group of users.